Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
780-784

Traffic flow prediction based on multi-spatial-temporal graph convolutional network

Dai Junminga
Cao Yanga,b
Shen Qinqinb
Shi Quana,b
a. College of Information Science & Technology, b. College of Transportation & Civil Engineering, Nantong University, Nantong Jiangsu 226019, China
DOI: 10.19734/j.issn.1001-3695.2021.08.0361

Abstract

Traffic flow forecasting is of great significance in the application of traffic management and urban planning. However, the existing forecasting methods cannot fully exploit the potential complex spatio-temporal correlations. In order to further explore the temporal and spatial characteristics of road network data to improve the prediction accuracy, this paper proposed an multi-spatial-temporal graph convolutional network(MST-GCN) model. Firstly, by using Chebyshev graph convolution(ChebNet) combined with gated recurrent unit(GRU) to construct spatio-temporal components to deeply mine the spatio-temporal correlation of nodes. Secondly, it extracted weekly, daily, and recent time sequence data separately, and entered three spatio-temporal components to deeply explore the time correlation between different time windows. Finally, it combined the spatio-temporal component and the encoder-decoder network structure to form the MST-GCN model. Experiments were conducted using the highway datasets PEMS04 and PEMS08 in the California Department of Transportation(Caltrans) performance evaluation system. The results show that the new model has significantly better performance than the gated recurrent unit model and the recently proposed diffusion convolutional recurrent neural network(DCRNN), temporal graph convolutional network(T-GCN), attention based spatial-temporal graph convolutional networks(ASTGCN) and spatial-temporal synchronous graph convolutional network(STSGCN) models.

Foundation Support

国家自然科学基金资助项目(61771265)
江苏高校“青蓝工程”项目
南通市科技计划项目(MS22021034,JC2021198)
南通市“226”科研项目(131320633045)
南通大学信息科学技术学院研究生科研与实践创新计划项目(NTUSISTPR21-007)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.08.0361
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Algorithm Research & Explore
Pages: 780-784
Serial Number: 1001-3695(2022)03-023-0780-05

Publish History

[2021-11-29] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

戴俊明, 曹阳, 沈琴琴, 等. 基于多时空图卷积网络的交通流预测 [J]. 计算机应用研究, 2022, 39 (3): 780-784. (Dai Junming, Cao Yang, Shen Qinqin, et al. Traffic flow prediction based on multi-spatial-temporal graph convolutional network [J]. Application Research of Computers, 2022, 39 (3): 780-784. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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